{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "71GTxOD71mEn"
   },
   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook, we will learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from 🤗 PEFT to fine-tune an image classification model by ONLY using **0.77%** of the original trainable parameters of the model. \n",
    "\n",
    "LoRA adds low-rank \"update matrices\" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://arxiv.org/abs/2106.09685). \n",
    "\n",
    "Let's get started by installing the dependencies. \n",
    "\n",
    "__*Note that this notebook builds on top the [official image classification example notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb).*__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0a_bETbqv4P7"
   },
   "source": [
    "## Install dependencies\n",
    "\n",
    "Here we're installing `peft` from source to ensure we have access to all the bleeding edge features of `peft`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z0_5BYt8hobv",
    "outputId": "aafcbc39-b972-493a-8922-2141b1621926"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m53.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers accelerate evaluate datasets loralib git+https://github.com/huggingface/peft -q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y8dSVHoIv7HC"
   },
   "source": [
    "## Authentication\n",
    "\n",
    "We will share our fine-tuned model at the end of training. So, to do that we just authenticate using our 🤗 token. This token is available from [here](https://huggingface.co/settings/tokens). If you don't have a 🤗 account already, we highly encourage you to do so; it's free!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359,
     "referenced_widgets": [
      "5d2f5fb454bc4c16b520e4e96381758f",
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      "ecf73dd75420460399bfd04d8cd81f90"
     ]
    },
    "id": "31Zv6rFYr37d",
    "outputId": "6476ebcf-6d71-4b7d-ee38-dc4f8e8d024e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token is valid.\n",
      "Your token has been saved in your configured git credential helpers (store).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AX7aJaIKjbCF"
   },
   "source": [
    "## Check the library versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ejkn8GBzh_DB",
    "outputId": "777afbdf-e026-43d8-8efa-80bb958d0ca3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "import transformers\n",
    "import accelerate\n",
    "import peft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A833xxo3ir28",
    "outputId": "da71ef1c-b6d7-43e2-a78b-23556785ef02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformers version: 4.26.0\n",
      "Accelerate version: 0.16.0\n",
      "PEFT version: 0.1.0.dev0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Transformers version: {transformers.__version__}\")\n",
    "print(f\"Accelerate version: {accelerate.__version__}\")\n",
    "print(f\"PEFT version: {peft.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Po1Ve9u5v_Ul"
   },
   "source": [
    "## Select a model checkpoint to fine-tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "vhvCQpP-isJr"
   },
   "outputs": [],
   "source": [
    "model_checkpoint = \"google/vit-base-patch16-224-in21k\"  # pre-trained model from which to fine-tune"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UKN3rMAsjgEz"
   },
   "source": [
    "## Load a dataset\n",
    "\n",
    "We're only loading the first 5000 instances from the training set of the [Food-101 dataset](https://huggingface.co/datasets/food101) to keep this example runtime short. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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     "base_uri": "https://localhost:8080/",
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    },
    "id": "rI0d2_liitUr",
    "outputId": "4ae986eb-6cbb-4d9f-bb99-1ffbb05ee835"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61b957d3b51643f78a921979072fe3b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/6.21k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71f7296ec9be4d9abe1af581722b40fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/5.56k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0b0cad40fbd461ca7bdcdbb5f442f57",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/10.3k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset food101/default to /root/.cache/huggingface/datasets/food101/default/0.0.0/7cebe41a80fb2da3f08fcbef769c8874073a86346f7fb96dc0847d4dfc318295...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fdf282b234fe4a1a8ab452ac04511b7d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.download.download_manager:Computing checksums of downloaded files. They can be used for integrity verification. You can disable this by passing ignore_verifications=True to load_dataset\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "849ac914c3cc49d29d619dd4f532d74c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Computing checksums: 100%|##########| 1/1 [00:14<00:00, 14.25s/it]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c580d3a6e99e48fab09b3ce799711802",
       "version_major": 2,
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      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13279e67c4d847e4846e2d34e8aac589",
       "version_major": 2,
       "version_minor": 0
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       "Downloading data:   0%|          | 0.00/1.47M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19ec79f5a5174aa3b26861a9662951d3",
       "version_major": 2,
       "version_minor": 0
      },
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       "Downloading data:   0%|          | 0.00/489k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fee4fba960ac41ed97984467da41f319",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/75750 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5718d031b9942f4b8bf331a8543db29",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/25250 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset food101 downloaded and prepared to /root/.cache/huggingface/datasets/food101/default/0.0.0/7cebe41a80fb2da3f08fcbef769c8874073a86346f7fb96dc0847d4dfc318295. Subsequent calls will reuse this data.\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"food101\", split=\"train[:5000]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pUjwa7G8jjgW"
   },
   "source": [
    "## Prepare datasets for training and evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Gg9xDW22yPD"
   },
   "source": [
    "1. Prepare `label2id` and `id2label` dictionaries. This will come in handy when performing inference and for metadata information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "GC3wK2aciz53",
    "outputId": "4b065fdc-6d89-46a2-88b5-b78c2d843036"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'baklava'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = dataset.features[\"label\"].names\n",
    "label2id, id2label = dict(), dict()\n",
    "for i, label in enumerate(labels):\n",
    "    label2id[label] = i\n",
    "    id2label[i] = label\n",
    "\n",
    "id2label[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xgHUxR_-2-h1"
   },
   "source": [
    "2. We load the image processor of the model we're fine-tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 474,
     "referenced_widgets": [
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      "a1981bfcdb6d401e9a521e18b511cf9d"
     ]
    },
    "id": "3hmq4a_fi2IX",
    "outputId": "f790d034-9efa-4a1a-e9f5-f3c6bd62add5"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d1b9ac29dcc41e08ada578916f20a3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)rocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb453686ce9f4342aaae9a9fb3500d2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "ViTImageProcessor {\n",
       "  \"do_normalize\": true,\n",
       "  \"do_rescale\": true,\n",
       "  \"do_resize\": true,\n",
       "  \"image_mean\": [\n",
       "    0.5,\n",
       "    0.5,\n",
       "    0.5\n",
       "  ],\n",
       "  \"image_processor_type\": \"ViTImageProcessor\",\n",
       "  \"image_std\": [\n",
       "    0.5,\n",
       "    0.5,\n",
       "    0.5\n",
       "  ],\n",
       "  \"resample\": 2,\n",
       "  \"rescale_factor\": 0.00392156862745098,\n",
       "  \"size\": {\n",
       "    \"height\": 224,\n",
       "    \"width\": 224\n",
       "  }\n",
       "}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoImageProcessor\n",
    "\n",
    "image_processor = AutoImageProcessor.from_pretrained(model_checkpoint)\n",
    "image_processor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EsZYbWKS3cPK"
   },
   "source": [
    "As one might notice, the `image_processor` has useful information on which size the training and evaluation images should be resized, stats that should be used to normalize the pixel values, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jKFuKh9P3E-e"
   },
   "source": [
    "3. Using the image processor we prepare transformation functions for the datasets. These functions will include augmentation and pixel scaling.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Zj33iIoCi3Uy"
   },
   "outputs": [],
   "source": [
    "from torchvision.transforms import (\n",
    "    CenterCrop,\n",
    "    Compose,\n",
    "    Normalize,\n",
    "    RandomHorizontalFlip,\n",
    "    RandomResizedCrop,\n",
    "    Resize,\n",
    "    ToTensor,\n",
    ")\n",
    "\n",
    "normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)\n",
    "train_transforms = Compose(\n",
    "    [\n",
    "        RandomResizedCrop(image_processor.size[\"height\"]),\n",
    "        RandomHorizontalFlip(),\n",
    "        ToTensor(),\n",
    "        normalize,\n",
    "    ]\n",
    ")\n",
    "\n",
    "val_transforms = Compose(\n",
    "    [\n",
    "        Resize(image_processor.size[\"height\"]),\n",
    "        CenterCrop(image_processor.size[\"height\"]),\n",
    "        ToTensor(),\n",
    "        normalize,\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "def preprocess_train(example_batch):\n",
    "    \"\"\"Apply train_transforms across a batch.\"\"\"\n",
    "    example_batch[\"pixel_values\"] = [train_transforms(image.convert(\"RGB\")) for image in example_batch[\"image\"]]\n",
    "    return example_batch\n",
    "\n",
    "\n",
    "def preprocess_val(example_batch):\n",
    "    \"\"\"Apply val_transforms across a batch.\"\"\"\n",
    "    example_batch[\"pixel_values\"] = [val_transforms(image.convert(\"RGB\")) for image in example_batch[\"image\"]]\n",
    "    return example_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X4IPqOeK3UKW"
   },
   "source": [
    "4. We split our mini dataset into training and validation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "_uplVC66i5Gd"
   },
   "outputs": [],
   "source": [
    "# split up training into training + validation\n",
    "splits = dataset.train_test_split(test_size=0.1)\n",
    "train_ds = splits[\"train\"]\n",
    "val_ds = splits[\"test\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KV5Mlf4e3X5K"
   },
   "source": [
    "5. We set the transformation functions to the datasets accordingly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "0QuiqyiXi9fN"
   },
   "outputs": [],
   "source": [
    "train_ds.set_transform(preprocess_train)\n",
    "val_ds.set_transform(preprocess_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sA1Cq97Ijpp8"
   },
   "source": [
    "## Load and prepare a model \n",
    "\n",
    "In this section, we first load the model we want to fine-tune. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "Zxgrg45Xty2S"
   },
   "outputs": [],
   "source": [
    "def print_trainable_parameters(model):\n",
    "    \"\"\"\n",
    "    Prints the number of trainable parameters in the model.\n",
    "    \"\"\"\n",
    "    trainable_params = 0\n",
    "    all_param = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_param += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_params += param.numel()\n",
    "    print(\n",
    "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param:.2f}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TYjC-A-44bHO"
   },
   "source": [
    "The `get_peft_model()` method that we will use in a moment wraps the original model to be fine-tuned as a `PeftModel`. So, it's important for us to initialize the original model correctly. As such, we initialize it by specifying the `label2id` and `id2label` so that `AutoModelForImageClassification` can initialize a append classification head to the underlying model, adapted for our dataset. We can confirm this from the warning below:\n",
    "\n",
    "```\n",
    "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 176,
     "referenced_widgets": [
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      "4a44332ff1224a19a5f1c18e2b827759",
      "7e1ac6f28fb340d3bde1e7b4893bb0aa"
     ]
    },
    "id": "3J5DokIqi-wV",
    "outputId": "66275479-0491-4db1-c265-333609b2dde2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9397ebc3ad2e4141a1405bb1bd0aa315",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/346M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at google/vit-base-patch16-224-in21k were not used when initializing ViTForImageClassification: ['pooler.dense.weight', 'pooler.dense.bias']\n",
      "- This IS expected if you are initializing ViTForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing ViTForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 85876325 || all params: 85876325 || trainable%: 100.00\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForImageClassification, TrainingArguments, Trainer\n",
    "\n",
    "model = AutoModelForImageClassification.from_pretrained(\n",
    "    model_checkpoint,\n",
    "    label2id=label2id,\n",
    "    id2label=id2label,\n",
    "    ignore_mismatched_sizes=True,  # provide this in case you're planning to fine-tune an already fine-tuned checkpoint\n",
    ")\n",
    "print_trainable_parameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1EqYCiTy5F9N"
   },
   "source": [
    "Also, take note of the number of total trainable parameters of `model`: it's 100%! We'll compare this number to that of the LoRA model.\n",
    "\n",
    "We now use the `PeftModel` to wrap `model` so that the \"update\" matrices are added to the respective places. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LNASJrqoi_8-",
    "outputId": "8088d2a6-b6fb-4ecc-f7c3-8f0797f4f6ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 667493 || all params: 86466149 || trainable%: 0.77\n"
     ]
    }
   ],
   "source": [
    "from peft import LoraConfig, get_peft_model\n",
    "\n",
    "config = LoraConfig(\n",
    "    r=16,\n",
    "    lora_alpha=16,\n",
    "    target_modules=[\"query\", \"value\"],\n",
    "    lora_dropout=0.1,\n",
    "    bias=\"none\",\n",
    "    modules_to_save=[\"classifier\"],\n",
    ")\n",
    "lora_model = get_peft_model(model, config)\n",
    "print_trainable_parameters(lora_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OKQeuUDhjC3E"
   },
   "source": [
    "Let's unpack what's going on here. \n",
    "\n",
    "In order for LoRA to take effect, we need to specify the target modules to `LoraConfig` so that `get_peft_model()` knows which modules inside our model needs to be amended with LoRA matrices. In this case, we're only interested in targetting the query and value matrices of the attention blocks of the base model. Since the parameters corresponding to these matrices are \"named\" with `query` and `value` respectively, we specify them accordingly in the `target_modules` argument of `LoraConfig`. \n",
    "\n",
    "We also specify `modules_to_save`. After we wrap our base model `model` with `get_peft_model()` along with the `config`, we get a new model where only the LoRA parameters are trainable (so-called \"update matrices\") while the pre-trained parameters are kept frozen. These include the parameters of the randomly initialized classifier parameters too. This is NOT we want when fine-tuning the base model on our custom dataset. To ensure that the classifier parameters are also trained, we specify `modules_to_save`. This also ensures that these modules are serialized alongside the LoRA trainable parameters when using utilities like `save_pretrained()` and `push_to_hub()`.  \n",
    "\n",
    "Regarding the other parameters:\n",
    "\n",
    "* `r`: The dimension used by the LoRA update matrices.\n",
    "* `alpha`: Scaling factor.\n",
    "* `bias`: Specifying if the `bias` parameters should be trained. `None` denotes none of the `bias` parameters will be trained. \n",
    "\n",
    "`r` and `alpha` together control the total number of final trainable parameters when using LoRA giving us the flexbility to balance a trade-off between end performance and compute efficiency.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mRbdQvEujHHP"
   },
   "source": [
    "We can also how many parameters we're actually training. Since we're interested in performing **parameter-efficient fine-tuning**, we should expect to notice a less number of trainable parameters from the `lora_model` in comparison to the original `model` which is indeed the case here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m6lBFL_D-w7k"
   },
   "source": [
    "## Training arguments\n",
    "\n",
    "We will leverage [🤗 Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) for fine-tuning. It accepts several arguments which we wrap using [`TrainingArguments`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "-iD2F33JjIzC"
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments, Trainer\n",
    "\n",
    "\n",
    "model_name = model_checkpoint.split(\"/\")[-1]\n",
    "batch_size = 128\n",
    "\n",
    "args = TrainingArguments(\n",
    "    f\"{model_name}-finetuned-lora-food101\",\n",
    "    remove_unused_columns=False,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    learning_rate=5e-3,\n",
    "    per_device_train_batch_size=batch_size,\n",
    "    gradient_accumulation_steps=4,\n",
    "    per_device_eval_batch_size=batch_size,\n",
    "    fp16=True,\n",
    "    num_train_epochs=5,\n",
    "    logging_steps=10,\n",
    "    load_best_model_at_end=True,\n",
    "    metric_for_best_model=\"accuracy\",\n",
    "    push_to_hub=True,\n",
    "    label_names=[\"labels\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7_SA1HcVAUNP"
   },
   "source": [
    "Some things to note here:\n",
    "\n",
    "* We're using a larger batch size since there is only a handful of parameters to train. \n",
    "* Larger learning rate than the normal (1e-5 for example). \n",
    "\n",
    "All of these things are a byproduct of the fact that we're training only a small number of parameters. This can potentially also reduce the need to conduct expensive hyperparameter tuning experiments. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XOlDXQnrjuc_"
   },
   "source": [
    "## Prepare evaluation metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
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      "47659b15eb284f06bf9735ca2e425646"
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    },
    "id": "guYecwzyjLmj",
    "outputId": "7efb445d-a442-4173-c869-9bc5be044e2b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86fca0e29e7a4dc8b2234134014958f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import evaluate\n",
    "\n",
    "metric = evaluate.load(\"accuracy\")\n",
    "\n",
    "\n",
    "# the compute_metrics function takes a Named Tuple as input:\n",
    "# predictions, which are the logits of the model as Numpy arrays,\n",
    "# and label_ids, which are the ground-truth labels as Numpy arrays.\n",
    "def compute_metrics(eval_pred):\n",
    "    \"\"\"Computes accuracy on a batch of predictions\"\"\"\n",
    "    predictions = np.argmax(eval_pred.predictions, axis=1)\n",
    "    return metric.compute(predictions=predictions, references=eval_pred.label_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mNeLDXaE_989"
   },
   "source": [
    "## Collation function\n",
    "\n",
    "This is used by `Trainer` to gather a batch of training and evaluation examples and prepare them in a format that is acceptable by the underlying model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "qIicZRMrjNC3"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "def collate_fn(examples):\n",
    "    pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n",
    "    labels = torch.tensor([example[\"label\"] for example in examples])\n",
    "    return {\"pixel_values\": pixel_values, \"labels\": labels}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YpWudVaJjwkx"
   },
   "source": [
    "## Train and evaluate"
   ]
  },
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       "Clean file runs/Feb07_02-50-30_319afa680fd7/1675738246.1183074/events.out.tfevents.1675738246.319afa680fd7.100…"
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       "Clean file runs/Feb07_02-43-38_319afa680fd7/events.out.tfevents.1675737843.319afa680fd7.7189.0:  10%|#        …"
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     },
     "metadata": {},
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      "text/plain": [
       "Download file runs/Feb07_02-50-30_319afa680fd7/events.out.tfevents.1675738403.319afa680fd7.10047.2: 100%|#####…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "bd9e7cb0f25445739ebcdff0d3112052",
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      "text/plain": [
       "Clean file runs/Feb07_02-50-30_319afa680fd7/events.out.tfevents.1675738403.319afa680fd7.10047.2: 100%|########…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77f7230186b14c628f5094f9fd8d82da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Clean file pytorch_model.bin:   0%|          | 1.00k/330M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cuda_amp half precision backend\n",
      "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "***** Running training *****\n",
      "  Num examples = 4500\n",
      "  Num Epochs = 5\n",
      "  Instantaneous batch size per device = 128\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 512\n",
      "  Gradient Accumulation steps = 4\n",
      "  Total optimization steps = 45\n",
      "  Number of trainable parameters = 667493\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='45' max='45' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [45/45 04:44, Epoch 5/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.506871</td>\n",
       "      <td>0.896000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.162700</td>\n",
       "      <td>0.189141</td>\n",
       "      <td>0.946000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.345100</td>\n",
       "      <td>0.144759</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.211600</td>\n",
       "      <td>0.150886</td>\n",
       "      <td>0.958000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.171100</td>\n",
       "      <td>0.149751</td>\n",
       "      <td>0.958000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 500\n",
      "  Batch size = 128\n",
      "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9\n",
      "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/config.json\n",
      "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/pytorch_model.bin\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-9/preprocessor_config.json\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 500\n",
      "  Batch size = 128\n",
      "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18\n",
      "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/config.json\n",
      "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/pytorch_model.bin\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-18/preprocessor_config.json\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 500\n",
      "  Batch size = 128\n",
      "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27\n",
      "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/config.json\n",
      "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/pytorch_model.bin\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27/preprocessor_config.json\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 500\n",
      "  Batch size = 128\n",
      "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36\n",
      "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/config.json\n",
      "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/pytorch_model.bin\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-36/preprocessor_config.json\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 500\n",
      "  Batch size = 128\n",
      "Saving model checkpoint to vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45\n",
      "Configuration saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/config.json\n",
      "Model weights saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/pytorch_model.bin\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-45/preprocessor_config.json\n",
      "Image processor saved in vit-base-patch16-224-in21k-finetuned-lora-food101/preprocessor_config.json\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n",
      "Loading best model from vit-base-patch16-224-in21k-finetuned-lora-food101/checkpoint-27 (score: 0.96).\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(\n",
    "    model,\n",
    "    args,\n",
    "    train_dataset=train_ds,\n",
    "    eval_dataset=val_ds,\n",
    "    tokenizer=image_processor,\n",
    "    compute_metrics=compute_metrics,\n",
    "    data_collator=collate_fn,\n",
    ")\n",
    "train_results = trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b2NENHxHCejv"
   },
   "source": [
    "In just a few minutes, we have a fine-tuned model with 96% validation accuracy. Also, note that we used a very small subset of the training dataset which is definitely impacting the results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "id": "_MAd2906jQKG",
    "outputId": "7b825531-4e8d-4666-d6fc-eaa1accf3bb6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "***** Running Evaluation *****\n",
      "  Num examples = 500\n",
      "  Batch size = 128\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='8' max='4' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [4/4 01:48]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.14475855231285095,\n",
       " 'eval_accuracy': 0.96,\n",
       " 'eval_runtime': 3.5725,\n",
       " 'eval_samples_per_second': 139.958,\n",
       " 'eval_steps_per_second': 1.12,\n",
       " 'epoch': 5.0}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluate(val_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qo_scDEyAQER"
   },
   "source": [
    "## Sharing your model and inference \n",
    "\n",
    "Once the fine-tuning is done, we can share the LoRA parameters with the community like so: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172,
     "referenced_widgets": [
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      "2bfd04824f2e4fd6844dd38e46dbbbdf",
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      "1902fbc4c1da4ffe90f0947e58eb5d48",
      "12a94351242444d7b0d23b8accc1824a"
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    "id": "TyQvIcnFzLIV",
    "outputId": "7ac2819e-080e-4940-9755-15c32832d9a6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Uploading the following files to sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101: adapter_config.json,adapter_model.bin\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "980d2f61332f414d9888b76e78774be4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e83fd078f467406da0baf26e18b39e89",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.bin:   0%|          | 0.00/2.69M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101/commit/64e17d1cda300041cbc67428242a3136060772a3', commit_message='Upload model', commit_description='', oid='64e17d1cda300041cbc67428242a3136060772a3', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repo_name = f\"sayakpaul/{model_name}-finetuned-lora-food101\"\n",
    "lora_model.push_to_hub(repo_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uCmWERkhSRNO"
   },
   "source": [
    "When we call `push_to_hub()` on the `lora_model`, only the LoRA parameters along with any modules specified in `modules_to_save` are saved. If we take a look at the [trained LoRA parameters](https://huggingface.co/sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101/blob/main/adapter_model.bin), we see that it's only **2.6 MB**! This greatly helps with portability especially when we're using a very large model to fine-tune (such as [BLOOM](https://huggingface.co/bigscience/bloom)). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kt57qe42Sr6E"
   },
   "source": [
    "Next, we see how to load the LoRA updated parameters along with our base model for inference. When we wrap a base model with `PeftModel` that modifications are DONE in place. So to mitigate any concerns that might stem from in place modifications, we newly initialize our base model just like we did earlier and construct our inference model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    "id": "IY78Ty570etw",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--google--vit-base-patch16-224-in21k/snapshots/1ba429d32753f33a0660b80ac6f43a3c80c18938/config.json\n",
      "Model config ViTConfig {\n",
      "  \"_name_or_path\": \"google/vit-base-patch16-224-in21k\",\n",
      "  \"architectures\": [\n",
      "    \"ViTModel\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.0,\n",
      "  \"encoder_stride\": 16,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.0,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"apple_pie\",\n",
      "    \"1\": \"baby_back_ribs\",\n",
      "    \"2\": \"baklava\",\n",
      "    \"3\": \"beef_carpaccio\",\n",
      "    \"4\": \"beef_tartare\",\n",
      "    \"5\": \"beet_salad\",\n",
      "    \"6\": \"beignets\",\n",
      "    \"7\": \"bibimbap\",\n",
      "    \"8\": \"bread_pudding\",\n",
      "    \"9\": \"breakfast_burrito\",\n",
      "    \"10\": \"bruschetta\",\n",
      "    \"11\": \"caesar_salad\",\n",
      "    \"12\": \"cannoli\",\n",
      "    \"13\": \"caprese_salad\",\n",
      "    \"14\": \"carrot_cake\",\n",
      "    \"15\": \"ceviche\",\n",
      "    \"16\": \"cheesecake\",\n",
      "    \"17\": \"cheese_plate\",\n",
      "    \"18\": \"chicken_curry\",\n",
      "    \"19\": \"chicken_quesadilla\",\n",
      "    \"20\": \"chicken_wings\",\n",
      "    \"21\": \"chocolate_cake\",\n",
      "    \"22\": \"chocolate_mousse\",\n",
      "    \"23\": \"churros\",\n",
      "    \"24\": \"clam_chowder\",\n",
      "    \"25\": \"club_sandwich\",\n",
      "    \"26\": \"crab_cakes\",\n",
      "    \"27\": \"creme_brulee\",\n",
      "    \"28\": \"croque_madame\",\n",
      "    \"29\": \"cup_cakes\",\n",
      "    \"30\": \"deviled_eggs\",\n",
      "    \"31\": \"donuts\",\n",
      "    \"32\": \"dumplings\",\n",
      "    \"33\": \"edamame\",\n",
      "    \"34\": \"eggs_benedict\",\n",
      "    \"35\": \"escargots\",\n",
      "    \"36\": \"falafel\",\n",
      "    \"37\": \"filet_mignon\",\n",
      "    \"38\": \"fish_and_chips\",\n",
      "    \"39\": \"foie_gras\",\n",
      "    \"40\": \"french_fries\",\n",
      "    \"41\": \"french_onion_soup\",\n",
      "    \"42\": \"french_toast\",\n",
      "    \"43\": \"fried_calamari\",\n",
      "    \"44\": \"fried_rice\",\n",
      "    \"45\": \"frozen_yogurt\",\n",
      "    \"46\": \"garlic_bread\",\n",
      "    \"47\": \"gnocchi\",\n",
      "    \"48\": \"greek_salad\",\n",
      "    \"49\": \"grilled_cheese_sandwich\",\n",
      "    \"50\": \"grilled_salmon\",\n",
      "    \"51\": \"guacamole\",\n",
      "    \"52\": \"gyoza\",\n",
      "    \"53\": \"hamburger\",\n",
      "    \"54\": \"hot_and_sour_soup\",\n",
      "    \"55\": \"hot_dog\",\n",
      "    \"56\": \"huevos_rancheros\",\n",
      "    \"57\": \"hummus\",\n",
      "    \"58\": \"ice_cream\",\n",
      "    \"59\": \"lasagna\",\n",
      "    \"60\": \"lobster_bisque\",\n",
      "    \"61\": \"lobster_roll_sandwich\",\n",
      "    \"62\": \"macaroni_and_cheese\",\n",
      "    \"63\": \"macarons\",\n",
      "    \"64\": \"miso_soup\",\n",
      "    \"65\": \"mussels\",\n",
      "    \"66\": \"nachos\",\n",
      "    \"67\": \"omelette\",\n",
      "    \"68\": \"onion_rings\",\n",
      "    \"69\": \"oysters\",\n",
      "    \"70\": \"pad_thai\",\n",
      "    \"71\": \"paella\",\n",
      "    \"72\": \"pancakes\",\n",
      "    \"73\": \"panna_cotta\",\n",
      "    \"74\": \"peking_duck\",\n",
      "    \"75\": \"pho\",\n",
      "    \"76\": \"pizza\",\n",
      "    \"77\": \"pork_chop\",\n",
      "    \"78\": \"poutine\",\n",
      "    \"79\": \"prime_rib\",\n",
      "    \"80\": \"pulled_pork_sandwich\",\n",
      "    \"81\": \"ramen\",\n",
      "    \"82\": \"ravioli\",\n",
      "    \"83\": \"red_velvet_cake\",\n",
      "    \"84\": \"risotto\",\n",
      "    \"85\": \"samosa\",\n",
      "    \"86\": \"sashimi\",\n",
      "    \"87\": \"scallops\",\n",
      "    \"88\": \"seaweed_salad\",\n",
      "    \"89\": \"shrimp_and_grits\",\n",
      "    \"90\": \"spaghetti_bolognese\",\n",
      "    \"91\": \"spaghetti_carbonara\",\n",
      "    \"92\": \"spring_rolls\",\n",
      "    \"93\": \"steak\",\n",
      "    \"94\": \"strawberry_shortcake\",\n",
      "    \"95\": \"sushi\",\n",
      "    \"96\": \"tacos\",\n",
      "    \"97\": \"takoyaki\",\n",
      "    \"98\": \"tiramisu\",\n",
      "    \"99\": \"tuna_tartare\",\n",
      "    \"100\": \"waffles\"\n",
      "  },\n",
      "  \"image_size\": 224,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"apple_pie\": 0,\n",
      "    \"baby_back_ribs\": 1,\n",
      "    \"baklava\": 2,\n",
      "    \"beef_carpaccio\": 3,\n",
      "    \"beef_tartare\": 4,\n",
      "    \"beet_salad\": 5,\n",
      "    \"beignets\": 6,\n",
      "    \"bibimbap\": 7,\n",
      "    \"bread_pudding\": 8,\n",
      "    \"breakfast_burrito\": 9,\n",
      "    \"bruschetta\": 10,\n",
      "    \"caesar_salad\": 11,\n",
      "    \"cannoli\": 12,\n",
      "    \"caprese_salad\": 13,\n",
      "    \"carrot_cake\": 14,\n",
      "    \"ceviche\": 15,\n",
      "    \"cheese_plate\": 17,\n",
      "    \"cheesecake\": 16,\n",
      "    \"chicken_curry\": 18,\n",
      "    \"chicken_quesadilla\": 19,\n",
      "    \"chicken_wings\": 20,\n",
      "    \"chocolate_cake\": 21,\n",
      "    \"chocolate_mousse\": 22,\n",
      "    \"churros\": 23,\n",
      "    \"clam_chowder\": 24,\n",
      "    \"club_sandwich\": 25,\n",
      "    \"crab_cakes\": 26,\n",
      "    \"creme_brulee\": 27,\n",
      "    \"croque_madame\": 28,\n",
      "    \"cup_cakes\": 29,\n",
      "    \"deviled_eggs\": 30,\n",
      "    \"donuts\": 31,\n",
      "    \"dumplings\": 32,\n",
      "    \"edamame\": 33,\n",
      "    \"eggs_benedict\": 34,\n",
      "    \"escargots\": 35,\n",
      "    \"falafel\": 36,\n",
      "    \"filet_mignon\": 37,\n",
      "    \"fish_and_chips\": 38,\n",
      "    \"foie_gras\": 39,\n",
      "    \"french_fries\": 40,\n",
      "    \"french_onion_soup\": 41,\n",
      "    \"french_toast\": 42,\n",
      "    \"fried_calamari\": 43,\n",
      "    \"fried_rice\": 44,\n",
      "    \"frozen_yogurt\": 45,\n",
      "    \"garlic_bread\": 46,\n",
      "    \"gnocchi\": 47,\n",
      "    \"greek_salad\": 48,\n",
      "    \"grilled_cheese_sandwich\": 49,\n",
      "    \"grilled_salmon\": 50,\n",
      "    \"guacamole\": 51,\n",
      "    \"gyoza\": 52,\n",
      "    \"hamburger\": 53,\n",
      "    \"hot_and_sour_soup\": 54,\n",
      "    \"hot_dog\": 55,\n",
      "    \"huevos_rancheros\": 56,\n",
      "    \"hummus\": 57,\n",
      "    \"ice_cream\": 58,\n",
      "    \"lasagna\": 59,\n",
      "    \"lobster_bisque\": 60,\n",
      "    \"lobster_roll_sandwich\": 61,\n",
      "    \"macaroni_and_cheese\": 62,\n",
      "    \"macarons\": 63,\n",
      "    \"miso_soup\": 64,\n",
      "    \"mussels\": 65,\n",
      "    \"nachos\": 66,\n",
      "    \"omelette\": 67,\n",
      "    \"onion_rings\": 68,\n",
      "    \"oysters\": 69,\n",
      "    \"pad_thai\": 70,\n",
      "    \"paella\": 71,\n",
      "    \"pancakes\": 72,\n",
      "    \"panna_cotta\": 73,\n",
      "    \"peking_duck\": 74,\n",
      "    \"pho\": 75,\n",
      "    \"pizza\": 76,\n",
      "    \"pork_chop\": 77,\n",
      "    \"poutine\": 78,\n",
      "    \"prime_rib\": 79,\n",
      "    \"pulled_pork_sandwich\": 80,\n",
      "    \"ramen\": 81,\n",
      "    \"ravioli\": 82,\n",
      "    \"red_velvet_cake\": 83,\n",
      "    \"risotto\": 84,\n",
      "    \"samosa\": 85,\n",
      "    \"sashimi\": 86,\n",
      "    \"scallops\": 87,\n",
      "    \"seaweed_salad\": 88,\n",
      "    \"shrimp_and_grits\": 89,\n",
      "    \"spaghetti_bolognese\": 90,\n",
      "    \"spaghetti_carbonara\": 91,\n",
      "    \"spring_rolls\": 92,\n",
      "    \"steak\": 93,\n",
      "    \"strawberry_shortcake\": 94,\n",
      "    \"sushi\": 95,\n",
      "    \"tacos\": 96,\n",
      "    \"takoyaki\": 97,\n",
      "    \"tiramisu\": 98,\n",
      "    \"tuna_tartare\": 99,\n",
      "    \"waffles\": 100\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"model_type\": \"vit\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_channels\": 3,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"patch_size\": 16,\n",
      "  \"qkv_bias\": true,\n",
      "  \"transformers_version\": \"4.26.0\"\n",
      "}\n",
      "\n",
      "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--google--vit-base-patch16-224-in21k/snapshots/1ba429d32753f33a0660b80ac6f43a3c80c18938/pytorch_model.bin\n",
      "Some weights of the model checkpoint at google/vit-base-patch16-224-in21k were not used when initializing ViTForImageClassification: ['pooler.dense.weight', 'pooler.dense.bias']\n",
      "- This IS expected if you are initializing ViTForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing ViTForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "323eb0d9dade4c4fa3a9ad2b973dcbe1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)\"adapter_model.bin\";:   0%|          | 0.00/2.69M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from peft import PeftConfig, PeftModel\n",
    "\n",
    "\n",
    "config = PeftConfig.from_pretrained(repo_name)\n",
    "model = model = AutoModelForImageClassification.from_pretrained(\n",
    "    config.base_model_name_or_path,\n",
    "    label2id=label2id,\n",
    "    id2label=id2label,\n",
    "    ignore_mismatched_sizes=True,  # provide this in case you're planning to fine-tune an already fine-tuned checkpoint\n",
    ")\n",
    "# Load the Lora model\n",
    "inference_model = PeftModel.from_pretrained(model, repo_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F9QDze9AXH8n"
   },
   "source": [
    "Don't worry about the warnings, they're harmless. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qb19xGKDW1XS"
   },
   "source": [
    "Let's now fetch a sample for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200
    },
    "id": "HxJAOWPGU1nL",
    "outputId": "ab0e4295-325e-4f21-b0c2-c2b09dd05a7a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=275x183 at 0x7F7B550B60D0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import requests\n",
    "\n",
    "url = \"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/beignets.jpeg\"\n",
    "image = Image.open(requests.get(url, stream=True).raw)\n",
    "image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dFuqZgmCW4cu"
   },
   "source": [
    "We first instantiate an `image_processor` from the underlying model repo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dN4x_pj-VQx8",
    "outputId": "b4fddd49-2f10-48e2-de31-0cefd20d405d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file preprocessor_config.json from cache at /root/.cache/huggingface/hub/models--sayakpaul--vit-base-patch16-224-in21k-finetuned-lora-food101/snapshots/fa2503cc7d91e0dd69728c1dc66ed80d7bd3289b/preprocessor_config.json\n",
      "Image processor ViTImageProcessor {\n",
      "  \"do_normalize\": true,\n",
      "  \"do_rescale\": true,\n",
      "  \"do_resize\": true,\n",
      "  \"image_mean\": [\n",
      "    0.5,\n",
      "    0.5,\n",
      "    0.5\n",
      "  ],\n",
      "  \"image_processor_type\": \"ViTImageProcessor\",\n",
      "  \"image_std\": [\n",
      "    0.5,\n",
      "    0.5,\n",
      "    0.5\n",
      "  ],\n",
      "  \"resample\": 2,\n",
      "  \"rescale_factor\": 0.00392156862745098,\n",
      "  \"size\": {\n",
      "    \"height\": 224,\n",
      "    \"width\": 224\n",
      "  }\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "image_processor = AutoImageProcessor.from_pretrained(repo_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dc0rCwC5XAaL"
   },
   "source": [
    "We then prepare the sample for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "57C6tcdnVYu1",
    "outputId": "f164dd91-8679-482e-fb10-2f530d4ea9f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "# prepare image for the model\n",
    "encoding = image_processor(image.convert(\"RGB\"), return_tensors=\"pt\")\n",
    "print(encoding.pixel_values.shape)"
   ]
  },
  {
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   },
   "source": [
    "And run inference!"
   ]
  },
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   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "YyQznW5WViMc",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class: beignets\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# forward pass\n",
    "with torch.no_grad():\n",
    "    outputs = inference_model(**encoding)\n",
    "    logits = outputs.logits\n",
    "\n",
    "predicted_class_idx = logits.argmax(-1).item()\n",
    "print(\"Predicted class:\", inference_model.config.id2label[predicted_class_idx])"
   ]
  }
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